ReviewMaterials (Basel, Switzerland)2023
Application of Machine Learning in Material Synthesis and Property Prediction.
Review in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
21 citing papers in PubMed.
- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Dynamic Ensemble Learning with Transfer Learning for Fatigue Performance Prediction in Ni-Based Superalloys.Materials (Basel, Switzerland) · 2026Article
- Application of Machine Learning in Predicting the Properties of Two-Dimensional Semiconductor Materials.Nanomaterials (Basel, Switzerland) · 2026Review
- Bridging laboratory findings and artificial intelligence for the design of TlInTeScientific reports · 2026Article
- Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery.Biomimetics (Basel, Switzerland) · 2026Review
- Data-Driven Inverse Design of Silver Nanoparticle Size for Controlled Synthesis Across Multiple Systems Using Conditional Generative Models.Materials (Basel, Switzerland) · 2026Article
- A machine learning approach for predicting osmotic coefficients and deriving activity coefficients in alkyl ammonium salts.Scientific reports · 2026Article
- BEmXRD-Nets framework for novel machine learning models to predict crystal energy with diversity structures.Scientific reports · 2026Article
- Article
- From Pixels to Predictions: Integrating Machine Learning and Digital Image Correlation for Damage Identification in Engineering Materials.Materials (Basel, Switzerland) · 2025Review
- Symmetry is the Key to the Design of Reticular Frameworks.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Article
- Statistical and ANN Modeling of Corrosion Behavior of Austenitic Stainless Steels in Aqueous Environments.Materials (Basel, Switzerland) · 2025Article
- Machine learning-assisted stiffness prediction in high-cell-density bioprinting.Bio-design and manufacturing · 2025Article
- The influence of frequency and temperature on the AC-conductivity in [Formula: see text] semiconductor single crystal.Scientific reports · 2025Article
- A review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
- Catalytic Reactor-Utilized Ammonia Adsorption, Absorption, and Storage Materials: Mechanism, Nanostructure, andACS sustainable chemistry & engineering · 2024Review
- Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.Journal of cheminformatics · 2024Article
- Machine Learning as a "Catalyst" for Advancements in Carbon Nanotube Research.Nanomaterials (Basel, Switzerland) · 2024Review
- Anti-solvent materials enhanced structural and optical properties on ambiently fabricated perovskite thin films.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.